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Detecting Spam Zombies by Monitoring Outgoing Messages Zhenhai Duan Department of Computer Science Florida State University

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Outline Motivation and background SPOT algorithm on detecting compromised machines Performance evaluation Summary 2

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Motivation Botnet becoming a major security issue –Spamming, DDoS, and identity theft Hard to defend botnet based attacks –Sheer volume, wide spread Lack of effective method to detect bots in local networks 3

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Motivation Utility-based online detection method SPOT –Detecting subset of compromised machines involved in spamming Bots increasingly used in sending spam –70% - 80% of all spam from bots in recent years –In response to blacklisting –Spamming provides key economic incentive for controller 4

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Network Model Machines in a network –Either compromised H 1 or normal H 0 – How to detect if a machine compromised as msgs pass SPOT sequentially? –Sequential Probability Ratio Test (SPRT) 5

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Sequential Probability Ratio Test Statistical method for testing –Null hypothesis against alternative hypothesis One-dimensional random walk –With two boundaries corresponding to hypotheses 6 A B

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SPRT Advantages –Online algorithm Applying to observations arriving sequentially –Fast detection Minimizing average number of observation required –Controlled results False positive and false negative errors can be bounded by user-specified thresholds 7

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SPRT X denote a Bernoulli random variable with unknown parameter θ SPRT tests null hypothesis H 0 θ = θ 0 against alternative hypothesis H 1 θ = θ 1 8

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SPRT How likely to have sequence of X 1, X 2, …, X n, under H 1 and H 0, respectively? 9

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SPRT Test Process Given two constant A and B, where A < B, at each step n, compute How to determine A and B –Let α and β be user-desired false positive and negative rates 10

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SPRT Bounds Relationship between actual false positive α’ and false negative β’ and desired ones α and β Average number of observation to reach decision 11

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SPOT Detection Algorithm Based on SPRT –H 1 : machine is compromised –H 0 : machine is normal Maintain Λ n for each IP observed Update Λ n in each step Compare Λ n to A and B Terminate when B is approached Restart when A is approached – after resetting Λ n 12

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Determining SPOT Parameters Four parameters: α, β, θ 0, θ 1 –α, β are user desired error rates, normally in range 0.01 to 0.05 –Ideally, θ 0 and θ 1 should be probability a normal and compromised machine send spam –SPOT does not require precise knowledge of θ 0 and θ 1 An imprecise (but reasonably) knowledge of θ 0 and θ 1 will only affect N In practice, they can model the false positive and detection rate of spam filter 13

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Averaged Number of Observations Required β = 0.01 14

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Trace-based Performance Evaluation Two month email trace received on FSU campus net SpamAssassin and anti-virus software –About 73% of all emails are spam 15

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Sending IP Addresses 16 –FSU has higher percentage of mixed IP addresses –FSU has higher percentage of IP addresses sending virus

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Performance of SPOT –Α = 0.01, β = 0.01, θ 0 = 0.2, θ 1 = 0.9 –110 confirmed by virus information –16 confirmed by high spam sending percentage (> 98%) 62.5% of these are dynamic IP –6 cannot be confirmed by either way –7 machines SPOT identified as normal carried virus 17

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Number of Actual Observations 18

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Impacts of Dynamic IP Addresses SPOT assumes one-to-one mapping between IP address and machine Intuitively, dynamic IP will not have any major impacts, given fast detection of SPOT 19

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Distribution of Spam in Each Cluster –T = 30 minutes –90% of clusters >= 10 spam –96% of clusters >= 3 spam 20

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Discussions Practical deployment issues –Msgs may pass a few relay servers before leaving network –Method 1: deploy SPOT at each relay server –Method 2: identify originating machine by Received header Limitation –IID assumption of message arrivals 21

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Summary SPOT –Effective and efficient spam zombie detection system –Based Sequential Probability Ratio Test A utility-based detection scheme –How to generalize the idea to detect compromised machines used for other purposes? 22

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